Class PlotAccessor (1.29.0) 

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

Examples: For Series:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> ser = bpd.Series([1, 2, 3, 3])
>>> plot = ser.plot(kind='hist', title="My plot")

For DataFrame:

>>> df = bpd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> plot = df.plot(title="DataFrame Plot")

Parameters
Name Description
data Series or DataFrame

The object for which the method is called.
kind str

The kind of plot to produce: - 'line' : line plot (default) - 'hist' : histogram - 'area' : area plot - 'scatter' : scatter plot (DataFrame only)

Methods

area

area(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    stacked: bool = True,
    **kwargs
)

Draw a stacked area plot. An area plot displays quantitative data visually.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

Draw an area plot based on basic business metrics:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(
...     {
...         'sales': [3, 2, 3, 9, 10, 6],
...         'signups': [5, 5, 6, 12, 14, 13],
...         'visits': [20, 42, 28, 62, 81, 50],
...     },
...     index=["01-31", "02-28", "03-31", "04-30", "05-31", "06-30"]
... )
>>> ax = df.plot.area()

Area plots are stacked by default. To produce an unstacked plot, pass stacked=False:

>>> ax = df.plot.area(stacked=False)

Draw an area plot for a single column:

>>> ax = df.plot.area(y='sales')

Draw with a different x:

>>> df = bpd.DataFrame({
...     'sales': [3, 2, 3],
...     'visits': [20, 42, 28],
...     'day': [1, 2, 3],
... })
>>> ax = df.plot.area(x='day')
Parameters
Name Description
x label or position, optional

Coordinates for the X axis. By default uses the index.
y label or position, optional

Column to plot. By default uses all columns.
stacked bool, default True

Area plots are stacked by default. Set to False to create a unstacked plot.
sampling_n int, default 100

Number of random items for plotting.
sampling_random_state int, default 0

Seed for random number generator.
Returns
Type Description
matplotlib.axes.Axes or numpy.ndarray Area plot, or array of area plots if subplots is True.

bar

bar(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

Draw a vertical bar plot.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

Basic plot.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame({'lab':['A', 'B', 'C'], 'val':[10, 30, 20]})
>>> ax = df.plot.bar(x='lab', y='val', rot=0)

Plot a whole dataframe to a bar plot. Each column is assigned a distinct color, and each row is nested in a group along the horizontal axis.

>>> speed = [0.1, 17.5, 40, 48, 52, 69, 88]
>>> lifespan = [2, 8, 70, 1.5, 25, 12, 28]
>>> index = ['snail', 'pig', 'elephant',
...          'rabbit', 'giraffe', 'coyote', 'horse']
>>> df = bpd.DataFrame({'speed': speed, 'lifespan': lifespan}, index=index)
>>> ax = df.plot.bar(rot=0)

Plot stacked bar charts for the DataFrame.

>>> ax = df.plot.bar(stacked=True)

If you don’t like the default colours, you can specify how you’d like each column to be colored.

>>> axes = df.plot.bar(
...     rot=0, subplots=True, color={"speed": "red", "lifespan": "green"}
... )
Parameters
Name Description
x label or position, optional

Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
y label or position, optional

Allows plotting of one column versus another. If not specified, all numerical columns are used.
Returns
Type Description
matplotlib.axes.Axes or numpy.ndarray Area plot, or array of area plots if subplots is True.

hist

hist(by: typing.Optional[typing.Sequence[str]] = None, bins: int = 10, **kwargs)

Draw one histogram of the DataFrame’s columns.

A histogram is a representation of the distribution of data. This function groups the values of all given Series in the DataFrame into bins and draws all bins in one matplotlib.axes.Axes. This is useful when the DataFrame's Series are in a similar scale.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> import numpy as np
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(np.random.randint(1, 7, 6000), columns=['one'])
>>> df['two'] = np.random.randint(1, 7, 6000) + np.random.randint(1, 7, 6000)
>>> ax = df.plot.hist(bins=12, alpha=0.5)
Parameters
Name Description
by str or sequence, optional

Column in the DataFrame to group by. It is not supported yet.
bins int, default 10

Number of histogram bins to be used.
Returns
Type Description
class matplotlib.AxesSubplot: A histogram plot.

line

line(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    **kwargs
)

Plot Series or DataFrame as lines. This function is useful to plot lines using DataFrame's values as coordinates.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame(
...     {
...         'one': [1, 2, 3, 4],
...         'three': [3, 6, 9, 12],
...         'reverse_ten': [40, 30, 20, 10],
...     }
... )
>>> ax = df.plot.line(x='one')
Parameters
Name Description
x label or position, optional

Allows plotting of one column versus another. If not specified, the index of the DataFrame is used.
y label or position, optional

Allows plotting of one column versus another. If not specified, all numerical columns are used.
color str, array-like, or dict, optional

The color for each of the DataFrame's columns. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A sequence of color strings referred to by name, RGB or RGBA code, which will be used for each column recursively. For instance ['green','yellow'] each column's %(kind)s will be filled in green or yellow, alternatively. If there is only a single column to be plotted, then only the first color from the color list will be used. - A dict of the form {column name : color}, so that each column will be colored accordingly. For example, if your columns are called a and b, then passing {'a': 'green', 'b': 'red'} will color %(kind)ss for column a in green and %(kind)ss for column b in red.
sampling_n int, default 100

Number of random items for plotting.
sampling_random_state int, default 0

Seed for random number generator.
Returns
Type Description
matplotlib.axes.Axes or np.ndarray of them An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True.

scatter

scatter(
    x: typing.Optional[typing.Hashable] = None,
    y: typing.Optional[typing.Hashable] = None,
    s: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    c: typing.Optional[
        typing.Union[typing.Hashable, typing.Sequence[typing.Hashable]]
    ] = None,
    **kwargs
)

Create a scatter plot with varying marker point size and color.

This function calls pandas.plot to generate a plot with a random sample of items. For consistent results, the random sampling is reproducible. Use the sampling_random_state parameter to modify the sampling seed.

Examples:

Let's see how to draw a scatter plot using coordinates from the values in a DataFrame's columns.

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> df = bpd.DataFrame([[5.1, 3.5, 0], [4.9, 3.0, 0], [7.0, 3.2, 1],
...                    [6.4, 3.2, 1], [5.9, 3.0, 2]],
...                   columns=['length', 'width', 'species'])
>>> ax1 = df.plot.scatter(x='length',
...                       y='width',
...                       c='DarkBlue')

And now with the color determined by a column as well.

>>> ax2 = df.plot.scatter(x='length',
...                       y='width',
...                       c='species',
...                       colormap='viridis')
Parameters
Name Description
x int or str

The column name or column position to be used as horizontal coordinates for each point.
y int or str

The column name or column position to be used as vertical coordinates for each point.
s str, scalar or array-like, optional

The size of each point. Possible values are: - A string with the name of the column to be used for marker's size. - A single scalar so all points have the same size.
c str, int or array-like, optional

The color of each point. Possible values are: - A single color string referred to by name, RGB or RGBA code, for instance 'red' or '#a98d19'. - A column name or position whose values will be used to color the marker points according to a colormap.
sampling_n int, default 100

Number of random items for plotting.
sampling_random_state int, default 0

Seed for random number generator.
Returns
Type Description
matplotlib.axes.Axes or np.ndarray of them An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True.

PlotAccessor

PlotAccessor(data)

Make plots of Series or DataFrame with the matplotlib backend.

Examples: For Series:

>>> import bigframes.pandas as bpd
>>> bpd.options.display.progress_bar = None
>>> ser = bpd.Series([1, 2, 3, 3])
>>> plot = ser.plot(kind='hist', title="My plot")

For DataFrame:

>>> df = bpd.DataFrame({'length': [1.5, 0.5, 1.2, 0.9, 3],
...                   'width': [0.7, 0.2, 0.15, 0.2, 1.1]},
...                   index=['pig', 'rabbit', 'duck', 'chicken', 'horse'])
>>> plot = df.plot(title="DataFrame Plot")
Parameters
Name Description
data Series or DataFrame

The object for which the method is called.
kind str

The kind of plot to produce: - 'line' : line plot (default) - 'hist' : histogram - 'area' : area plot - 'scatter' : scatter plot (DataFrame only)
Returns
Type Description
matplotlib.axes.Axes or np.ndarray of them An ndarray is returned with one matplotlib.axes.Axes per column when subplots=True.